GPU-Native Adaptive Mesh Refinement with Application to Lattice Boltzmann Simulations

The Lattice Boltzmann Method (LBM) has garnered significant interest in General-Purpose Graphics Processing Unit (GPGPU) programming for computational fluid dynamics due to its straightforward GPU parallelization and could benefit greatly from Adaptive Mesh Refinement (AMR). AMR can assist in efficiently resolving flows with regions of interest requiring a high degree of resolution. An AMR scheme that could manage a computational mesh entirely on the GPU without intermediate data transfers to/from the host device would provide a substantial speedup to GPU-accelerated solvers, however, implementations commonly employ CPU/hybrid frameworks instead, due to lack of a recursive data structure. A block-based GPU-native algorithm will be presented for AMR in the context of GPGPU programming and implemented in an open-source C++ code. The meshing code is equipped with a Lattice Boltzmann solver for assessing performance. Different AMR approaches and consequences in implementation are considered before careful selection of data structures enabling efficient refinement and coarsening compatible with single instruction multiple data architecture is detailed. Inter-level communication is achieved by tricubic interpolation and standard spatial averaging. Although the present open-source implementation is tailored for LBM simulations, the outlined grid refinement procedure is compatible with solvers for cell-centered block-structured grids. Link to repository: https://github.com/KhodrJ/AGALComment: 30 pages, 16 figure

The therapeutic effects of adipose-derived mesenchymal stem cells on obesity and its associated diseases in diet-induced obese mice

Obesity is a global public health concern associated with increased risk of several comorbidities. Due to the limited effectiveness of current therapies, new treatment strategies are needed. Our aim was to examine the effect of adipose-derived mesenchymal stem cells (AD-MSCs) on obesity and its associated diseases in a diet-induced obese (DIO) animal model. C57BL6 mice were fed with either high fat diet (HFD) or CHOW diet for 15 weeks. Obese and lean mice were then subjected to two doses of AD-MSCs intraperitoneally. Mice body weight and composition; food intake; blood glucose levels; glycated hemoglobin (HbA1c), intraperitoneal glucose tolerance test and atherogenic index of plasma (AIP) were measured. Pro-inflammatory cytokines, tumor necrosis factor-α and interleukin-6, were also determined. AD-MSCs treatment reduced blood glucose levels, HbA1c and AIP as well as improved glucose tolerance in DIO mice. In addition, MSCs caused significant attenuation in the levels of inflammatory mediators in HFD-fed mice. Taken together, AD-MSCs were effective in treating obesity-associated diabetes in an animal model as well as protective against cardiovascular diseases as shown by AIP, which might be partly due to the attenuation of inflammatory mediators. Thus, AD-MSCs may offer a promising therapeutic potential in counteracting obesity-related diseases in patients.This study was funded by Beirut Arab University (BAU) and the Lebanese National Council for Scientific Research (CNRS-L)

Author Correction: The therapeutic effects of adipose-derived mesenchymal stem cells on obesity and its associated diseases in diet-induced obese mice

An amendment to this paper has been published and can be accessed via a link at the top of the paper

An Enhanced Python-Based Open-Source Particle Image Velocimetry Software for Use with Central Processing Units

Particle Image Velocimetry (PIV) is a widely used experimental technique for measuring flow. In recent years, open-source PIV software has become more popular as it offers researchers and practitioners enhanced computational capabilities. Software development for graphical processing unit (GPU) architectures requires careful algorithm design and data structure selection for optimal performance. PIV software, optimized for central processing units (CPUs), offer an alternative to specialized GPU software. In the present work, an improved algorithm for the OpenPIV–Python software (Version 0.25.1, OpenPIV, Tel Aviv-Yafo, Israel) is presented and implemented under a traditional CPU framework. The Python language was selected due to its versatility and widespread adoption. The algorithm was also tested on a supercomputing cluster, a workstation, and Google Colaboratory during the development phase. Using a known velocity field, the algorithm precisely captured the time-average flow, momentary velocity fields, and vortices

Effect of Intraoperative High Positive End-Expiratory Pressure (PEEP) With Recruitment Maneuvers vs Low PEEP on Postoperative Pulmonary Complications in Obese Patients: A Randomized Clinical Trial (vol 321, pg 2292, 2019)

status: publishe

Effect of Intraoperative High Positive End-Expiratory Pressure (PEEP) with Recruitment Maneuvers vs Low PEEP on Postoperative Pulmonary Complications in Obese Patients: A Randomized Clinical Trial

Importance: An intraoperative higher level of positive end-expiratory positive pressure (PEEP) with alveolar recruitment maneuvers improves respiratory function in obese patients undergoing surgery, but the effect on clinical outcomes is uncertain. Objective: To determine whether a higher level of PEEP with alveolar recruitment maneuvers decreases postoperative pulmonary complications in obese patients undergoing surgery compared with a lower level of PEEP. Design, Setting, and Participants: Randomized clinical trial of 2013 adults with body mass indices of 35 or greater and substantial risk for postoperative pulmonary complications who were undergoing noncardiac, nonneurological surgery under general anesthesia. The trial was conducted at 77 sites in 23 countries from July 2014-February 2018; final follow-up: May 2018. Interventions: Patients were randomized to the high level of PEEP group (n = 989), consisting of a PEEP level of 12 cm H2O with alveolar recruitment maneuvers (a stepwise increase of tidal volume and eventually PEEP) or to the low level of PEEP group (n = 987), consisting of a PEEP level of 4 cm H2O. All patients received volume-controlled ventilation with a tidal volume of 7 mL/kg of predicted body weight. Main Outcomes and Measures: The primary outcome was a composite of pulmonary complications within the first 5 postoperative days, including respiratory failure, acute respiratory distress syndrome, bronchospasm, new pulmonary infiltrates, pulmonary infection, aspiration pneumonitis, pleural effusion, atelectasis, cardiopulmonary edema, and pneumothorax. Among the 9 prespecified secondary outcomes, 3 were intraoperative complications, including hypoxemia (oxygen desaturation with Spo2 ≤92% for >1 minute). Results: Among 2013 adults who were randomized, 1976 (98.2%) completed the trial (mean age, 48.8 years; 1381 [69.9%] women; 1778 [90.1%] underwent abdominal operations). In the intention-to-treat analysis, the primary outcome occurred in 211 of 989 patients (21.3%) in the high level of PEEP group compared with 233 of 987 patients (23.6%) in the low level of PEEP group (difference, -2.3% [95% CI, -5.9% to 1.4%]; risk ratio, 0.93 [95% CI, 0.83 to 1.04]; P =.23). Among the 9 prespecified secondary outcomes, 6 were not significantly different between the high and low level of PEEP groups, and 3 were significantly different, including fewer patients with hypoxemia (5.0% in the high level of PEEP group vs 13.6% in the low level of PEEP group; difference, -8.6% [95% CI, -11.1% to 6.1%]; P <.001). Conclusions and Relevance: Among obese patients undergoing surgery under general anesthesia, an intraoperative mechanical ventilation strategy with a higher level of PEEP and alveolar recruitment maneuvers, compared with a strategy with a lower level of PEEP, did not reduce postoperative pulmonary complications. Trial Registration: ClinicalTrials.gov Identifier: NCT02148692